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Temporal precision in the neural code and the timescales of natural vision

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  • Daniel A. Butts

    (School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
    The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York 10021, USA)

  • Chong Weng

    (State University of New York College of Optometry, New York, New York 10036, USA)

  • Jianzhong Jin

    (State University of New York College of Optometry, New York, New York 10036, USA)

  • Chun-I Yeh

    (State University of New York College of Optometry, New York, New York 10036, USA
    University of Connecticut, Storrs, Connecticut 06269, USA)

  • Nicholas A. Lesica

    (School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA)

  • Jose-Manuel Alonso

    (State University of New York College of Optometry, New York, New York 10036, USA)

  • Garrett B. Stanley

    (School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA)

Abstract

Precision in vision In mammalian visual system, spikes evoked by visual stimuli have millisecond-scale timing even though the relevant timescales of visual processing themselves are much slower. It has therefore long been debated whether spike timing itself carries some form of the neural code. Now experiments in the lateral geniculate nucleus of cats, the part of the brain that is the primary processor of visual information, show that spike timing precision is not absolute for all classes of visual stimuli. Rather, the degree of precision is relative to the timescale of the stimulus, and this relatively high level of precision is required to construct an accurate representation of the stimulus.

Suggested Citation

  • Daniel A. Butts & Chong Weng & Jianzhong Jin & Chun-I Yeh & Nicholas A. Lesica & Jose-Manuel Alonso & Garrett B. Stanley, 2007. "Temporal precision in the neural code and the timescales of natural vision," Nature, Nature, vol. 449(7158), pages 92-95, September.
    • Handle: RePEc:nat:nature:v:449:y:2007:i:7158:d:10.1038_nature06105
    DOI: 10.1038/nature06105
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    Citations

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    Cited by:

    1. Baysal, Veli & Yılmaz, Ergin, 2021. "Chaotic Signal Induced Delay Decay in Hodgkin-Huxley Neuron," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Sean T Kelly & Jens Kremkow & Jianzhong Jin & Yushi Wang & Qi Wang & Jose-Manuel Alonso & Garrett B Stanley, 2014. "The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-13, January.
    3. Gaëlle Desbordes & Jianzhong Jin & Chong Weng & Nicholas A Lesica & Garrett B Stanley & Jose-Manuel Alonso, 2008. "Timing Precision in Population Coding of Natural Scenes in the Early Visual System," PLOS Biology, Public Library of Science, vol. 6(12), pages 1-11, December.
    4. Bryce Allen Bagley & Blake Bordelon & Benjamin Moseley & Ralf Wessel, 2020. "Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-25, February.
    5. Zane N Aldworth & Alexander G Dimitrov & Graham I Cummins & Tomáš Gedeon & John P Miller, 2011. "Temporal Encoding in a Nervous System," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-19, May.
    6. Rava Azeredo da Silveira & Michael J Berry II, 2014. "High-Fidelity Coding with Correlated Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-25, November.
    7. Cheng Ly & Brent Doiron, 2009. "Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-12, April.
    8. Ovidiu F Jurjuţ & Danko Nikolić & Wolf Singer & Shan Yu & Martha N Havenith & Raul C Mureşan, 2011. "Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-15, February.
    9. James M McFarland & Yuwei Cui & Daniel A Butts, 2013. "Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.

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